---
license: cc-by-4.0
language:
  - en
task_categories:
  - image-to-text
  - text-to-image
  - other
tags:
  - cad
  - 3d
  - benchmark
  - design
  - engineering
  - evaluation
  - rubric
size_categories:
  - n<1K
pretty_name: MUSE - Multi-modal CAD Understanding & Synthesis Evaluation
configs:
  - config_name: default
    data_files:
      - split: test
        path: metadata.jsonl
---

# MUSE: A CAD Design Benchmark with Multi-modal Ground Truth and Rubric-based Evaluation

MUSE is a benchmark of **106 CAD design cases** for evaluating language and
multi-modal models on engineering-grade 3D design tasks. Each case pairs a
natural-language design specification with multi-view ground-truth artefacts
(2D engineering drawings + 3D rendered images) and a hand-crafted, rubric-style
evaluation guide.

## Why this benchmark

Most CAD/3D benchmarks evaluate either pure geometry reconstruction or
free-form text-to-3D quality, but not both at once and not with the
**engineering correctness** lens (assembly topology, joint design, tolerance,
function). MUSE fills this gap by:

1. providing a **textual design specification** as input,
2. offering two complementary ground-truth views — a four-view dimensioned
   engineering drawing and a 3D STP render — so judges can reason about both
   geometry and manufacturability,
3. shipping a **per-case rubric** with explicit pass/fail criteria for
   assembly readiness, joint design, tolerance, functional adaptation, and
   more.

## Dataset structure

```
.
├── README.md
├── LICENSE
├── metadata.jsonl                # one JSON object per case
└── cases/
    └── <case_id>/
        ├── design_description.md     # the input prompt
        ├── <case_id>.png             # 4-view dimensioned engineering drawing (from SVG)
        ├── <case_id>_stp_render.png  # 3D STP rendered image
        └── evaluation_rubric.md      # rubric for judging a generated design
```

Each line of `metadata.jsonl` looks like:

```json
{
  "case_id": "bookshelf",
  "design_description": "cases/bookshelf/design_description.md",
  "svg_png":           "cases/bookshelf/bookshelf.png",
  "stp_render":        "cases/bookshelf/bookshelf_stp_render.png",
  "evaluation_rubric": "cases/bookshelf/evaluation_rubric.md"
}
```

For 9 organic / curved-vase cases (`vase_wave_*`, `wave_vase`), where 2D SVG
projection cannot capture the surface geometry, the 3D STP render image is
intentionally identical to the engineering-drawing PNG — i.e. the curved
silhouette image is used as both ground-truth views.

## How to use

```python
from datasets import load_dataset

ds = load_dataset("dongxiaoyu/MUSE", split="test")
print(ds[0])
# {
#   'case_id': 'bookshelf',
#   'design_description': 'cases/bookshelf/design_description.md',
#   ...
# }
```

To resolve a referenced file:

```python
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="dongxiaoyu/MUSE",
    repo_type="dataset",
    filename=ds[0]["design_description"],
)
print(open(path).read())
```

## Recommended evaluation protocol

For each case:

1. Feed `design_description.md` to the model under test as the user prompt.
2. Collect the model's design output (e.g. CAD code, SVG, mesh, or rendered
   image, depending on the modality being evaluated).
3. Compare the model output against the ground-truth pair
   (`<case>.png`, `<case>_stp_render.png`).
4. Apply each criterion in `evaluation_rubric.md` as an independent 0/1 score
   and report per-rubric pass-rates as well as the overall mean.

A reference judge prompt and breakdown JSON schema are included in the
companion paper.

## Cases

The benchmark covers 106 design cases across the following families:
chairs, tables, bookshelves & shelves, CNC furniture (TV stands, shoe racks,
desks), pegboards, pen holders, stools, vases, and miscellaneous household
fixtures (handle, hook, toothbrush holder, etc.).

## Responsible AI metadata

A complete Croissant 1.0 metadata file with the MLCommons RAI extension is
shipped at the root of the repository as `croissant.json` (raw URL:
[`croissant.json`](https://huggingface.co/datasets/dongxiaoyu/MUSE/resolve/main/croissant.json)).
The fields below summarize the RAI sections required by the NeurIPS 2026
Datasets & Benchmarks track.

### Uses (intended and out-of-scope)

**Intended use.** Benchmarking large language models and vision-language
models on (a) interpreting natural-language CAD design specifications,
(b) generating or reasoning about engineering CAD geometry, and
(c) reasoning about manufacturability concepts including assembly topology,
joint type selection, tolerance, and functional adaptation. The 0/1 rubric
score, aggregated across criteria and cases, is meant to be reported
alongside qualitative inspection of model outputs.

**Out-of-scope use.** This dataset is *not* designed as a training corpus.
It contains only 106 cases and would not provide useful coverage as
pre-training data; using it for training risks contamination of any
benchmark report computed on the same cases. The benchmark also does not
validate physical fabricability — passing every rubric is a necessary but
not sufficient condition for a design to be machinable in the real world.

### Bias, risks, and limitations

**Biases.** Domain coverage is biased toward Western-style household
objects and small fixtures (chairs, tables, shelves, vases, holders,
pegboards, stools); automotive, aerospace, electronics, and large
civil-engineering domains are absent. Manufacturing methods are limited
to CNC milling, 3D printing, traditional joinery, and a few
adhesive/fastening methods (no casting, forging, or metal additive).
Materials are dominated by timber, plastic, and metal. Component counts
skew toward 10–60 parts. All design specifications are in English. Because
geometry seeds were generated with LLM assistance, stylistic or naming
biases of the underlying foundation models may propagate into the cases.

**Limitations.** Only 4-view orthographic + isometric drawings and a single
3D render are provided as ground truth — there is no live STEP-vs-STEP
geometric comparison built into the rubric. STEP files are watertight
but tolerances and clearances are not validated against physical
fabrication. Rubric scoring is binary (Pass / Fail) per criterion, so
mostly-correct designs are not credited proportionally. Each rubric is
authored against one specific reference design and may be brittle when
applied to substantially different but equally valid alternative designs.
With 106 cases the benchmark cannot finely separate models with very
small performance differences.

### Personal and sensitive information

The dataset contains **no** personally identifiable information.
There are no human-subject data, no images of people, no audio or video,
no health, financial, geographic-location, biometric, or genetic data.
None of the categories tracked by the Croissant RAI specification appear
in the dataset.

### Source data

The benchmark is **fully original**. CAD geometry seeds were generated
with assistance from leading commercial large language models (GPT-4
family, Claude, Gemini) between **2026-04 and 2026-05**, executed in a
sandboxed CadQuery environment, then curated and verified by the authors.
The natural-language design specifications and the per-case evaluation
rubrics were authored entirely by the human authors. **No third-party
copyrighted CAD models, scraped web content, or derived datasets are
included.**

### Synthetic data

All CAD geometry, all design descriptions, and all rubrics are synthetic
or human-authored. There is no real-world telemetry, sensor capture, or
photographic content in the dataset.

### Social impact

*Positive.* Rigorous evaluation of LLM/VLM ability on engineering tasks
supports research into AI-assisted design tools that augment human
designers, lower the barrier to entry for novice mechanical designers,
and enable faster iteration on early-stage product design.

*Negative.* Improved model capabilities on engineering-grade CAD
generation could be misused to reproduce proprietary mechanical designs
without authorization or to bypass professional review in safety-critical
applications. The dataset itself contains no operational hazards (no
weapons, surveillance content, or dual-use components).

## License

This dataset is released under the
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
license. You are free to share and adapt the material for any purpose,
including commercially, provided that you give appropriate credit.

## Citation

```bibtex
  @misc{dong2026muse,
    title         = {MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation},
    author        = {Xiaoyu Dong and Zhi Li and Xiao-Ming Wu},
    year          = {2026},
    eprint        = {2605.28579},
    archivePrefix = {arXiv},
    primaryClass  = {cs.AI},
    url           = {https://arxiv.org/abs/2605.28579}
  }
```